964 resultados para Sanctions Committee
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This is part of the finding aid to the Graduate School and University Center (GSUC) Archives. Record Group t IV is records from various GSUC committees.
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Tackling a problem requires mostly, an ability to read it, conceptualize it, represent it, define it, and then applying the necessary mechanisms to solve it. This may sound self-evident except when the problem to be tackled happens to be “complex, “ “ill-structured,” and/or “wicked.” Corruption is one of those kinds of problems. Both in its global and national manifestations it is ill-structured. Where it is structural in nature, endemic and pervasive, it is perhaps even wicked. Qualities of the kind impose modest expectations regarding possibilities of any definitive solution to this insidious phenomenon. If so, it may not suffice to address the problem of corruption using existing categories of law and/or good governance, which overlook the “long-term memory” of the collective and cultural specific dimensions of the subject. Such socio-historical conditions require focusing on the interactive and self-reproducing networks of corruption and attempting to ‘subvert’ that phenomenon’s entire matrix. Concepts such as collective responsibility, collective punishment and sanctions are introduced as relevant categories in the structural, as well as behavioral, subversion of some of the most prevalent aspects of corruption. These concepts may help in the evolving of a new perspective on corruption fighting strategies.
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In a natural experiment, this paper studies the impact of an informal sanctioning mechanism on individuals’ voluntary contribution to a public good. Cross-country skiers’ actual cash contributions in two ski resorts, one with and one without an informal sanctioning system, are used. I find the contributing share to be higher in the informal sanctioning system (79 percent) than in the non-sanctioning system (36 percent). Previous studies in one-shot public good situations have found an increasing conditional contribution (CC) function, i.e. the relationship between expected average contributions of other group members and the individual’s own contribution. In contrast, the present results suggest that the CC-function in the non-sanctioning system is non-increasing at high perceived levels of others’ contribution. This relationship deserves further testing in lab.
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Tackling a problem requires mostly, an ability to read it, conceptualize it, represent it, define it, and then applying the necessary mechanisms to solve it. This may sound self-evident except when the problem to be tackled happens to be “complex, “ “ill-structured,” and/or “wicked.” Corruption is one of those kinds of problems. Both in its global and national manifestations it is ill-structured. Where it is structural in nature, endemic and pervasive, it is perhaps even wicked. Qualities of the kind impose modest expectations regarding possibilities of any definitive solution to this insidious phenomenon. If so, it may not suffice to address the problem of corruption using existing categories of law and/or good governance, which overlook the “long-term memory” of the collective and cultural specific dimensions of the subject. Such socio-historical conditions require focusing on the interactive and self-reproducing networks of corruption and attempting to ‘subvert’ that phenomenon’s entire matrix. Concepts such as collective responsibility, collective punishment and sanctions are introduced as relevant categories in the structural, as well as behavioral, subversion of some of the most prevalent aspects of corruption. These concepts may help in the evolving of a new perspective on corruption fighting strategies.
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This study looks at the historical context in which PACs developed, as well as the current legal environment in which they operate. It will also briefly discuss the legal and procedural challenges that candidates face and the ways in which PACs alleviate some of these pressures in ways that presidential committees cannot. An understanding of the strategic dilemmas which cause candidates to seek extraneous structures through which to establish campaign networks is essential to extrapolating the potential future of campaign finance strategy. Furthermore, this study provides an in-depth analysis of the state Commonwealth PACs both in terms of fundraising and spending, and discusses the central issues this state PAC strategy raises with respect to campaign finance law. The study will conclude with a look into the future of campaign financing and the role these state-level PACs may play if current rules are not revised.
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This study presents an approach to combine uncertainties of the hydrological model outputs predicted from a number of machine learning models. The machine learning based uncertainty prediction approach is very useful for estimation of hydrological models' uncertainty in particular hydro-metrological situation in real-time application [1]. In this approach the hydrological model realizations from Monte Carlo simulations are used to build different machine learning uncertainty models to predict uncertainty (quantiles of pdf) of the a deterministic output from hydrological model . Uncertainty models are trained using antecedent precipitation and streamflows as inputs. The trained models are then employed to predict the model output uncertainty which is specific for the new input data. We used three machine learning models namely artificial neural networks, model tree, locally weighted regression to predict output uncertainties. These three models produce similar verification results, which can be improved by merging their outputs dynamically. We propose an approach to form a committee of the three models to combine their outputs. The approach is applied to estimate uncertainty of streamflows simulation from a conceptual hydrological model in the Brue catchment in UK and the Bagmati catchment in Nepal. The verification results show that merged output is better than an individual model output. [1] D. L. Shrestha, N. Kayastha, and D. P. Solomatine, and R. Price. Encapsulation of parameteric uncertainty statistics by various predictive machine learning models: MLUE method, Journal of Hydroinformatic, in press, 2013.
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